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dc.contributor.authorPeyman, Mohammad-
dc.contributor.authorCopado Méndez, Pedro Jesús-
dc.contributor.authorTordecilla Madera, Rafael David-
dc.contributor.authorDo Carmo Martins, Leandro-
dc.contributor.authorXhafa, Fatos-
dc.contributor.authorJuan Pérez, Ángel Alejandro-
dc.contributor.otherUniversitat Politècnica de Catalunya-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.identifier.citationPeyman, M. [Mohammad], Copado, P. [Pedro ], Tordecilla, R.D. [Rafael D.], Do Carmo Martins, L.[Leandro], Xhafa, F. [Fatos] & Juan, A.A. [Angel A.]. (2021). Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems. Energies, 14(19), 1-26. doi: 10.3390/en14196309-
dc.description.abstractWith the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.en
dc.relation.ispartofEnergies, 2021, 14(19)-
dc.rightsCC BY-
dc.subjectedge computingen
dc.subjectInternet of thingsen
dc.subjectintelligent transportation systemsen
dc.subjectsmart citiesen
dc.subjectmachine learningen
dc.subjectagile optimizationen
dc.subjectinternet de les cosesca
dc.subjectinternet de las cosases
dc.subjectciutats intel·ligentsca
dc.subjectciudades inteligenteses
dc.subjectedge computingca
dc.subjectedge computinges
dc.subjectsistemas inteligentes de transporteses
dc.subjectsistemes intel·ligents de transportsca
dc.subjectaprendizaje automáticoes
dc.subjectaprenentatge automàticca
dc.subjectoptimización ágiles
dc.subjectoptimització àgilca
dc.subject.lcshSmart citiesen
dc.titleEdge computing and IoT analytics for agile optimization in intelligent transportation systems-
dc.subject.lemacCiutats intel·ligentsca
dc.subject.lcshesCiudades inteligenteses
dc.relation.projectIDinfo:eu-repo/grantAgreement/PID2019- 111100RB-C21/AEI/10.13039/501100011033-
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